The Kullback-Leibler divergence is a widespread dissimilarity measure between probability density functions, based on the Shannon entropy. Unfortunately, there is no analytic formula available to compute this divergence between mixture models, imposing the use of costly approximation algorithms. In order to reduce the computational burden when a lot of divergence evaluations are needed, we introduce a sub-class of the mixture models where the component parameters are shared between a set of mixtures and the only degree-of-freedom is the vector of weights of each mixture. This sharing allows to design extremely fast versions of existing dissimilarity measures between mixtures. We demonstrate the effectiveness of our approach by evaluating the quality of the ordering produced by our method on a real dataset.